3D printing-based lateral flow visual assay utilizing the dual-excitation property of nitrogen-doped carbon dots for the ratiometric determination and chemometric discrimination of flavonoids.

A ratiometric fluorescence sensor has been established based on dual-excitation carbon dots (D-CDs) for the detection of flavonoids (morin is chosen as the typical detecting model for flavonoids). D-CDs were prepared using microwave radiation with o-phenylenediamine and melamine and exhibit controllable dual-excitation behavior through the regulation of their concentration. Remarkably, the short-wavelength excitation of D-CDs can be quenched by morin owing to the inner filter effect, while the long-wavelength excitation remains insensitive, serving as the reference signal. This contributes to the successful design of an excitation-based ratiometric sensor. Based on the distinct and differentiated variation of excitation intensity, morin can be determined from 0.156 to 110 µM with a low detection limit of 0.156 µM. In addition, an intelligent and visually lateral flow sensing device is developed for the determination  of morin content in real samples with satisfying recoveries, which indicates the potential application for human health monitoring.

A sensitive lateral flow test strip sensor for visual detection of acid red 18 in food using bicentric-emission carbon dots.

Hazardous synthetic colorants have found widespread use in food production, and excessive consumption of these pigments can pose potential risks to human health. In this study, we propose an ultrasensitive fluorescence method for the analysis of Acid Red 18 (AR18) in food products. The method is based on the nitrogen-doped carbon dots (N-CDs) derived from tris and resorcinol through a hydrothermal way. The as-synthesized N-CDs exhibit two emission centers at 425 nm and 541 nm, corresponding to the excitation wavelengths of 377 nm and 465 nm, respectively. Upon the addition of AR18, the fluorescence intensity at 541 nm significantly decreases with a simultaneous, though less pronounced, reduction in the intensity at 425 nm, which is attributed to the localization of fluorescence resonance energy transfer (L-FRET). Specifically, a novel ratiometric fluorescent probe was constructed based on the extracted data from the 3D fluorescence excitation-emission matrix. This probe demonstrates a wide linear range from 0.0539 to 30 µM and a low limit of detection (LOD) of 53.9 nM. For practical applications, a portable fluorescent sensor based on a lateral flow test strip (LFTS) was designed for real-time monitoring of AR18. Color channel values were determined using a smartphone application, resulting in a satisfactory LOD of 75.3 nM. Furthermore, the suitability of the proposed ratiometric fluorescent probe was validated through the detection of AR18 in real food samples, consistently achieving recovery rates in the range of 99.7-101.4%. This research not only expands the scope of CDs in sensing fields, but also provides an effective strategy for the development of an excellent platform for real-time AR18 detection, contributing to public food safety.

Construction of fluorescent sensor array and three-dimensional microfluidic paper based analytical device for specific identification and visual determination of antibiotics in food.

For antibiotics misuse since the global outbreak of COVID 19, a novel strategy for discriminating and detecting antibiotics is proposed based on the graphene quantum dots with multi-doped heteroatoms including F, N and P (M-GQDs), which exhibit blue emission (419.0 nm) under the excitation of 336.0 nm. Specifically, the fluorescence of M-GQDs is quenched by tetracyclines (TCs) owing to inner filter effect (IFE) and enhanced by alkane-modified fluoroquinolones (AFQs), which is attributed to restricted conformational rotation based on π-π stacking, hydrogen-bonding and electrostatic interactions. Meanwhile, the electron-accepting property of oxazine ring in oxazine-modified fluoroquinolones (OFQs) increases emission peak at 498.0 nm and decreases emission peak at 419.0 nm as the color changes from blue to cyan. Moreover, a cascade system integrated with 3D microfluidic paper-based analytical device (3D-µPAD) is applied successfully for visually distinguishing three antibiotics, which shows great potential and versatility of M-GQDs for food safety monitoring.

Aggregation-induced bimodal excitation of nitrogen-doped carbon dots for ratiometric sensing of new coccine and solid-state multicolor lighting.

Trace detection of foodstuff pigments have gained increasing attention because of their close association with biological and environmental processes. Herein, we propose an innovative bimodal excitation nitrogen-doped carbon dots (N-CDs) for ratiometric sensing of new coccine (NC) pigment, which are synthesized by using melamine and o-phenylenediamine as precursors via solvothermal treatment. With the increase of the N-CDs concentration, N-CDs exhibit not only a concentration-dependent tunable color behavior, but also a novel aggregation-induced bimodal excitation phenomenon. Considering this distinctive bimodal excitation behavior, a ratiometric sensor based on N-CDs has been developed for the detection of the NC in different organic solvents due to the inner filter effect and fluorescence resonance energy transfer. The intensity ratio of two excitation signals is linear with the NC concentration in the range of 0.032-100 µM, and the limit of detection is as low as 32.1 nM. Meanwhile, we realize the design of multicolor-emission N-CDs/polymer films. All in all, this work presents a novel kind view of the mechanism of distinctive bimodal excitation of N-CDs, and further proposes an innovative ratiometric method for the screening analysis of NC in food samples and environmental pollutants.

Activated cascade effect for dual-mode ratiometric and smartphone-assisted visual detection of curcumin and F- based on nitrogen-doped carbon dots.

The growing persistence of harmful ion or drug molecular residues has always been considered as a matter of concern due to its importance in biological and environmental processes, which requires taking measures to maintain environmental health sustainably and effectively. Inspired by the multi-system and visual quantitative detection of nitrogen-doped carbon dots (N-CDs), we develop a novel cascade nano-system based on dual emission carbon dots for on-site visual quantitative detection of curcumin and fluoride ion (F-). Herein, tris (hydroxymethyl) aminomethane (Tris) and m-dihydroxybenzene (m-DHB) are elected as reaction precursors to synthesize dual-emission N-CDs by a one-step hydrothermal method. The obtained N-CDs exhibit dual emission peaks at 426 nm (blue) and 528 nm (green) with quantum yields of 53 % and 71 %, respectively. Then, trace curcumin and F- intelligent off-on-off sensing probe is formed by taking advantage of the activated cascade effect. As for the occurrence of inner filter effect (IFE) and fluorescence resonance energy transfer (FRET), the green fluorescence of N-CDs quenches remarkably, called as OFF initial state. Then the curcumin-F- complex leads to the hypochromatic shift of the absorption band from 532 to 430 nm, which activates the green fluorescence of N-CDs, named as ON state. Meanwhile, the blue fluorescence of N-CDs is quenched due to the FRET, called as OFF terminal state. This system shows good linear relationships from 0 to 35 µM and 0 to 40 µM with low detection limits of 29 nM and 42 nM for curcumin and F- ratiometric detection, respectively. Moreover, a smartphone-assisted analyzer is developed for on-site quantitative detection. Furthermore, we design a logic gate for logistics information storage, which proves the possibility of a logic gate based on N-CDs in practical application. Thus, our work will provide an effective strategy for environmental quantitative monitoring and information storage encryption.

Energy transfer mediated rapid and visual discrimination of tetracyclines and quercetin in food by using N, Cu Co-doped carbon dots.

The appearance of multi-drug resistant Escherichia coli makes the combination of tetracyclines (TCs) and quercetin (QCT) more common to fight stubborn bacterial infections so that the effective detections of TCs and QCT are essential and necessary. Here, a novel fluorescence probe for differentiating TCs and QCT is developed based on the nitrogen and copper co-doped carbon dots (N, Cu-CDs). The N, Cu-CDs are prepared from ethylene diamine tetraacetic acid (EDTA) and anhydrous copper chloride as precursors through hydrothermal process and exhibit bright blue fluorescence with excellent optical stability. With the presence of four tetracyclines (DOX, TC, CTC and OTC), the fluorescence intensity of N, Cu-CDs is quenched directly due to the internal filtration effect (IFE), and the detection limit obtained through single-signal fluorescence sensing is as low as 23.8 nM for DOX, 37.2 nM for TC, 43.8 nM for OTC and 28.8 nM for CTC. More remarkably, three dimensional ratiometric fluorescence probe for detecting QCT is proposed based on the appearance of another emission at (410 nm, 490 nm) due to electron transform (ET) process. This new method shows a good linear relationship in the range of 10-100 µM with a low detection limit of 59.3 nM. Furthermore, a dual-channel fluorescence sensing platform based on microfluidics paper-based analytical devices (µPADs) is developed for simultaneously visual discrimination of TCs (DOX is chosen as the typical detecting model for TCs) and QCT. This investigation provides a new way for the development of CDs as multifunction fluorescence probes.

Accurate Prediction of Hydration Sites of Proteins Using Energy Model With Atom Embedding.

We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites. In experiments on the OppA protein dataset used in previous studies and our selection of protein structures, our method achieves the highest model quality in terms of F1 score, compared to several previous studies.

Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction.

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. For all 13 data sets, Chemi-Net resulted in higher R2 values compared with the Cubist benchmark. The median R2 increase rate over Cubist was 26.7%. We expect that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.